检索结果-内蒙古大学图书馆

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

ISBN: (纸本)157735897X

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

引用

40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Automated Segmentation of Lateral Ventricle in MR Images Using Multi-scale Feature Fusion Convolutional Neural Network 25th

Automated Segmentation of Lateral Ventricle in MR Images Usi...

引用

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Ye, Fei Wang, Zhiqiang Hu, Kai Zhu, Sheng Gao, Xieping Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China College of Medical Imaging and Inspection Xiangnan University Chenzhou423000 China

ISBN: (纸本)9783030687793

Studies have shown that the expansion of the lateral ventricle is closely related to many neurodegenerative diseases, so the segmentation of the lateral ventricle plays an important role in the diagnosis of related diseases. However, traditional segmentation methods are subjective, laborious, and time-consuming. Furthermore, due to the uneven magnetic field, irregular, small, and discontinuous shape of every single slice, the segmentation of the lateral ventricle is still a great challenge. In this paper, we propose an efficient and automatic lateral ventricle segmentation method in magnetic resonance (MR) images using a multi-scale feature fusion convolutional neural network (MFF-Net). First, we create a multi-center clinical dataset with a total of 117 patient MR scans. This dataset comes from two different hospitals and the images have different sampling intervals, different ages, and distinct image dimensions. Second, we present a new multi-scale feature fusion module (MSM) to capture different levels of feature information of lateral ventricles through various receptive fields. In particular, MSM can also extract the multi-scale lateral ventricle region feature information to solve the problem of insufficient feature extraction of small object regions with the deepening of network structure. Finally, extensive experiments have been conducted to evaluate the performance of the proposed MFF-Net. In addition, to verify the performance of the proposed method, we compare MFF-Net with seven state-of-the-art segmentation models. Both quantitative results and visual effects show that our MFF-Net outperforms other models and can achieve more accurate segmentation performance. The results also indicate that our model can be applied in clinical practice and is a feasible method for lateral ventricle segmentation. © 2021, Springer Nature Switzerland AG.

关键词： Magnetic resonance

来源：评论

学校读者我要写书评

暂无评论

A spurious-free characteristic mode formulation based on surface integral equation for patch antenna structures

arXiv

引用

arXiv 2021年

作者： Fan, Kun Zhao, Ran Cheng, Guang Shang Huang, Zhi Xiang Hu, Jun Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Chengdu611731 China

Conventional surface integral equation (SIE)-based characteristic mode formulation for the patch antenna structure with a finite substrate is susceptible to the spurious (nonphysical) modes due to the dielectric part. To avoid the contamination of spurious modes, we propose a novel generalized eigenvalue formulation based on the electric field integral equation coupled Poggio-Miller-Chang-Harrington-Wu-Tsai (EFIEPMCHWT) equation. In this formulation, the real and imaginary parts of the exterior integral operators are chosen to construct the finalized weighting matrices, to connect radiated power of the characteristic current. Compared with other SIE-based methods, this equation doesn't need additional post-processing since it can effectively avoid spurious modes. Numerical results compared with the volume-surface integral equation (VSIE) are investigated to validate the accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

TransMixer: A Hybrid Transformer and CNN Architecture for Polyp Segmentation

TransMixer: A Hybrid Transformer and CNN Architecture for Po...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Huang, Yanglin Tan, Donghui Zhang, Yuan Li, Xuanya Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Affiliated Hospital of Xiangnan University Chengzhou423000 China Baidu Inc Beijing100085 China Xiangnan University Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Chenzhou423000 China

ISBN: (纸本)9781665468190

Learning how to fully extract global representations and local features is a key factor in improving the performance of polyp segmentation. In this paper, we explore the potential of combined techniques of Transformers and convolutional neural networks (CNNs) to address the challenges of polyp segmentation. Specifically, we present TransMixer, a hybrid interaction fusion architecture of the Transformer branch and the CNN branch, which is able to enhance the local details of global representations and the global context awareness of local features. To achieve this, we first bridge the semantic gap between the Transformer branch and the CNN branch through the Interaction Fusion Module (IFM), and then make full use of both respective properties to enhance polyp feature representations. After that, we further propose the Hierarchical Attention Module (HAM) to collect polyp semantic information from high-level features to gradually guide the recovery of polyp spatial information in low-level features. Quantitative and qualitative results show that the proposed model is more robust to various complex situations compared to existing methods, and achieves state-of-the-art performance in polyp segmentation. © 2022 IEEE.

关键词： Parallel architectures

来源：评论

学校读者我要写书评

暂无评论

Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens

arXiv

引用

arXiv 2024年

作者： Sun, Dengdi Pan, Yajie Lu, Andong Li, Chenglong Luo, Bin The Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education School of Artificial Intelligence Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

Many RGBT tracking researches primarily focus on modal fusion design, while overlooking the effective handling of target appearance changes. While some approaches have introduced historical frames or fuse and replace initial templates to incorporate temporal information, they have the risk of disrupting the original target appearance and accumulating errors over time. To alleviate these limitations, we propose a novel Transformer RGBT tracking approach, which mixes spatio-temporal multimodal tokens from the static multimodal templates and multimodal search regions in Transformer to handle target appearance changes, for robust RGBT tracking. We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates. We also use attention mechanisms to enhance the target features of multimodal template tokens by incorporating supplementary modal cues, and make the multimodal search region tokens interact with multimodal dynamic template tokens via attention mechanisms, which facilitates the conveyance of multimodal-enhanced target change information. Our module is inserted into the transformer backbone network and inherits joint feature extraction, search-template matching, and cross-modal interaction. Extensive experiments on three RGBT benchmark datasets show that the proposed approach maintains competitive performance compared to other state-of-the-art tracking algorithms while running at 39.1 FPS. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Spatial-Temporal Framework for Engagement Prediction 2nd

Fine-Grained Spatial-Temporal Framework for Engagement Pred...

引用

2nd CSIG Conference on Emotional Intelligence, CEI 2024

作者： Wang, Shanshan Zheng, Yong Wang, keyang Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Beijing China Institutes of Physical Science and Information Technology Anhui University Anhui China Zhejiang Dahua Technology Co. Ltd. Zhejiang China School of Computer Science and Technology Anhui University Anhui China

ISBN: (纸本)9789819650835

The engagement prediction task aims to identify the current level of involvement of individuals based on the information presented in video clips. In recent years, engagement prediction has attracted considerable attention due to its significance in real-life scenarios. We observed that an individual’s engagement status is often primarily manifested in the facial region. This observation prompted us to solve the engagement prediction problem by exploiting facial information to learn features related to engagement. In this paper, we propose a novel model for learning fine-grained features, called Fine-Grained Spatial-Temporal Framework (FGST). Specifically, we consider both spatial and temporal aspects. In the spatial domain, we propose an entropy-weighted method based on Fourier transform. By applying Fourier transform to video frames and introducing the concept of information entropy, we can segregate engagement-related information from engagement-unrelated information and mining difficult samples. This aids in enabling the model to learn features conducive to engagement prediction. In the temporal domain, we calculate the differences between adjacent frames and sum these differences to obtain regions of pixel variation between video frames, thereby generates saliency weight and improving model performance. Experiments on several public datasets demonstrate the proposed model can outperform the state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

A correlation-breaking interleaving of polar codes in concatenated systems

引用

Science China(Information Sciences) 2021年第10期64卷 270-271页

作者： Ya MENG Liping LI Chuan ZHANG Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education of China Anhui University National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories

Dear editor,Polar codes, along with a low complexity successive cancellation(SC) decoding, were discovered in [1] by Ar?kan. It is shown in [2] that the bit errors with the SC decoding are correlated. To improve the performance, other decoding techniques [3, 4] and concatenation schemes [5, 6] were studied. In this study, the theoretical aspects of the error correlation are investigated to improve the decoding performance of polar codes. This is the first attempt to utilize the error correlation to improve the performance of polar codes.

关键词： Information Systems and Communication Service

来源：评论

学校读者我要写书评

暂无评论

Entity Alignment Through Joint Utilization of Multiple Pretrained Models for Attribution Relationship

Entity Alignment Through Joint Utilization of Multiple Pretr...

引用

IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Liu Yujia Xu Tao Su Zhehan Li Xinsong Xue Kaipeng Liu Yuxin Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Lanzhou Gansu Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Lanzhou Gansu College of Information Engineering Shanghai Maritime University Shanghai

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

Most existing models for entity alignment tasks are based on a single pre-training mode. Through multiple experiments, it has been verified that a single pre-training model is no longer sufficient to meet the needs of current entity alignment tasks. Based on considerations of multiple pre-training models, this paper proposes an entity alignment method that utilizes multiple pre-training models. The purpose of this method is to address the limited accuracy in entity alignment tasks, with a particular focus on separately pre-training the embedding of entity attribute information and link relationship information for matching. To achieve the goal of improving entity alignment accuracy, this paper adopts joint alignment using pre-training with BERT and GloVe word embeddings. Experimental evaluations are conducted on the DBP15k dataset, and the results demonstrate that leveraging multiple pre-training models for attribute and relationship joint alignment can enhance model performance. The evaluation metrics of the model on three different language datasets, Hits@1 and MRR, are as follows: 0.899, 0.930, 0.964, and 0.932, 0.951, 0.975, respectively. These metrics indicate that the model performs well in aligning entities and outperforms current mainstream entity alignment methods.

关键词： Training Measurement Deep learning Cloud computing Accuracy Computational modeling Semantics

来源：评论

学校读者我要写书评

暂无评论

Design of 0.8-3.7 GHz Broadband asymmetric multi-node directional coupler

Design of 0.8-3.7 GHz Broadband asymmetric multi-node direct...

引用

International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Ruikang Wang Bo Wu Meng Kong Jingyang Wang Yue Ma Xianliang Wu Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal University Hefei China

ISBN: (纸本)9781665482271

In this paper, a 15 dB asymmetric broadband directional coupler for 4G and 5G communication is designed. The physical size of the coupler is $13\ \text{mm}\times 60\ \text{mm}$ . The dielectric constant is 4.4 and the thickness of single layer plate is 2 mm. The couplers operate in the 0.8-3.7 GHz band. The coupler has the advantages of bandwidth width, convenient processing and installation. ANSYS HFSS 2019 simulation results shows that in the 0.8-3.7 GHz band, the return loss (S 11 ) is more than 16.4 dB, the insertion loss (S 21 ) is from 0.14 dB to 1.18 dB, the coupling degree (S 31 ) is about 15 dB, and the isolation degree (S 41 ) is more than 29 dB. The method of multi-section coupling line cascade is adopted, to achieve the requirements of bandwidth width. The directivity of the coupler is increased by loading the branches to meet the design requirements of the 15 dB coupler.

关键词： Couplings Microwave technology Power transmission lines 5G mobile communication Simulation Millimeter wave technology Broadband communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：